A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment
With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022-10-01
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| Series: | Future Internet |
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| Online Access: | https://www.mdpi.com/1999-5903/14/10/301 |
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| author | Amit Sagu Nasib Singh Gill Preeti Gulia Jyotir Moy Chatterjee Ishaani Priyadarshini |
| author_facet | Amit Sagu Nasib Singh Gill Preeti Gulia Jyotir Moy Chatterjee Ishaani Priyadarshini |
| author_sort | Amit Sagu |
| collection | DOAJ |
| description | With the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data from the network and categorizes it as “Attack” or “Normal”. Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques. |
| first_indexed | 2024-03-09T09:47:05Z |
| format | Article |
| id | doaj.art-9ac8e027fc4d47c6a852c9f079303253 |
| institution | Directory Open Access Journal |
| issn | 1999-5903 |
| language | English |
| last_indexed | 2024-03-09T09:47:05Z |
| publishDate | 2022-10-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Future Internet |
| spelling | doaj.art-9ac8e027fc4d47c6a852c9f0793032532023-12-02T00:30:16ZengMDPI AGFuture Internet1999-59032022-10-01141030110.3390/fi14100301A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT EnvironmentAmit Sagu0Nasib Singh Gill1Preeti Gulia2Jyotir Moy Chatterjee3Ishaani Priyadarshini4Department of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, IndiaDepartment of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, IndiaDepartment of Computer Science & Applications, Maharshi Dayanand University, Rohtak 124001, IndiaDepartment of Information Technology, Lord Buddha Education Foundation, Kathmandu 44600, NepalSchool of Information, University of California, Berkeley, CA 94720-5800, USAWith the growth of the Internet of Things (IoT), security attacks are also rising gradually. Numerous centralized mechanisms have been introduced in the recent past for the detection of attacks in IoT, in which an attack recognition scheme is employed at the network’s vital point, which gathers data from the network and categorizes it as “Attack” or “Normal”. Nevertheless, these schemes were unsuccessful in achieving noteworthy results due to the diverse necessities of IoT devices such as distribution, scalability, lower latency, and resource limits. The present paper proposes a hybrid model for the detection of attacks in an IoT environment that involves three stages. Initially, the higher-order statistical features (kurtosis, variance, moments), mutual information (MI), symmetric uncertainty, information gain ratio (IGR), and relief-based features are extracted. Then, detection takes place using Gated Recurrent Unit (GRU) and Bidirectional Long Short-Term Memory (Bi-LSTM) to recognize the existence of network attacks. For improving the classification accuracy, the weights of Bi-LSTM are optimally tuned via a self-upgraded Cat and Mouse Optimizer (SU-CMO). The improvement of the employed scheme is established concerning a variety of metrics using two distinct datasets which comprise classification accuracy, and index, f-measure and MCC. In terms of all performance measures, the proposed model outperforms both traditional and state-of-the-art techniques.https://www.mdpi.com/1999-5903/14/10/301Bi-LSTMGRUIoToptimizationsecurity attack detection |
| spellingShingle | Amit Sagu Nasib Singh Gill Preeti Gulia Jyotir Moy Chatterjee Ishaani Priyadarshini A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment Future Internet Bi-LSTM GRU IoT optimization security attack detection |
| title | A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment |
| title_full | A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment |
| title_fullStr | A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment |
| title_full_unstemmed | A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment |
| title_short | A Hybrid Deep Learning Model with Self-Improved Optimization Algorithm for Detection of Security Attacks in IoT Environment |
| title_sort | hybrid deep learning model with self improved optimization algorithm for detection of security attacks in iot environment |
| topic | Bi-LSTM GRU IoT optimization security attack detection |
| url | https://www.mdpi.com/1999-5903/14/10/301 |
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